Asset Management, GIS and LiDAR Projects

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We do business with a lot clients these days who are looking for an “Enterprise Asset Management” system . They use this term during the procurement process, but in a lot of cases their requirements are centered on Work Management and barely scratch the surface of Asset Management. This is easy to do since most of an organization’s daily activities are focused solely on today’s maintenance of their Asset Infrastructure, but there is very little focus on how they will manage and maintain assets into the future. Our clients are always answering questions related to the fiscal activities centered on asset performance. The questions from management are centered around:

How much are we spending on maintenance?

How long does it take us to respond to and fix an issue?

Are we meeting Federally mandated requirements?

Anything else relating to money…

The IAM defines asset management as the “coordinated activity of an organization to realize value from assets”. This involves the “balancing of costs, opportunities and risks against the desired performance of assets, to achieve the organizational objectives.” An additional objective is to “minimize the whole-life cost of assets but there may be other critical factors such as risk or business continuity to be considered objectively in this decision making.” All of these factors can be combined together to make informed decisions regarding how assets are managed and maintained throughout their life-cycle. These decisions involve monetary expenditures, but they also involve strategic thinking centered on the “How” and “Why” to fix an asset as well as “When” and “Which” portions of this process. This is the “Strategic” piece of an Asset Management system.

Work Management is one small component of Asset Management. It is typically focused on the day-to-day operations and expenditures related to operating and maintaining asset infrastructure. The Work done against an asset can track cost information, but can also be used to build a strategy around the operations and maintenance related to that asset. This strategy focuses on the “How” and “Why”. It answers what “Activity” should be completed for an asset (Install, Maintain, Repair, Replace) and “Why” (It’s old, looks bad, is dangerous, could cause injury, get us sued) this should happen. Next, it answers “When” (now, next year, or never) an asset should be maintained as well as “Which” (most critical, most likely to fail, the Mayor’s sewer line) assets should get priority. All of these factors are important and ALL of them should be utilized when making a Strategic Asset Management decision. Be reminded that Work Management is only one component of this decision-making criteria which is applied to an overall Strategic Asset Management plan.

DOTs across the Country are mandated by the Federal Government to keep track of their roadway assets and to report against these assets to receive Federal funding for their maintenance and repair. Many DOTs conduct Roadway Characteristics Inventories (RCI) on an annual basis to update and maintain their data relative to these assets. Traditionally, this has been completed using a boots-on-the-ground approach which has been very effective at building these inventories. Many DOTs are experimenting with other technologies, namely mobile LiDAR, to conduct these inventories and to achieve many other benefits from the 3D data captured in the process.

The next graphic illustrates the typical technology solution utilized for these projects. It is composed of the Riegl VMX-450 LiDAR unit, coupled with High-definition Right-of-Way (ROW) imagery. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a density of 0.3 foot at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable.

Right-of-Way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being extracted from the point cloud. In this example, the DOT has digitized Shoulder, Driveway Culvert Ends, and Drainage Features (Culverts, Ditches and Bottom of Swale). Additional Features such as Signs, Signals, Striping, and Markings will also be extracted and then reported to the Feds on an annual basis. The mobile LiDAR data provides a 3D surface from which to compile the data and then the ROW imagery can be used for contextual purposes to support attribution. This methodology provides an effective process that can be used to create 3D vector layers and accurate attribution used to build a robust Enterprise GIS.

Both the ROW imagery and the mobile LiDAR can be used to collect and extract the RCI data efficiently for the DOTs and provides the DOT with a robust data set that can be leveraged into the future. The ROW imagery is typically used to map features at a mapping-grade level while the LiDAR can vary a bit in accuracy. Since the relative accuracy inherent in the LiDAR is very precise, it is used to conduct dimensional measurements related to clearances, sign panel sizes, lane widths, and other measurements that require a higher precision.

The DOT utilizes the derivative products from this RCI exercise to report to the Feds in a way that is pretty basic, but effective to achieve their level of funding. For example, the data capture is very technical in nature and focuses on high precision and accuracy. Then, the RCI data is extracted from this source data, maintaining a level of precision that is dictated by the source data. Then, the DOT takes this precise data and aggregates it up to a higher level and reports the total number of Signs or the lineal feet of guardrail. Even though the reporting of this data is pretty basic in nature, the origins of the data can still have precision and accuracy and can be used for other purposes related to Engineering Design or Asset Management.

In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to collect and report against RCI variables for DOTs. This methodology promotes a safe working environment for both the DOT worker and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data which can be utilized for other purposes within the same Agency.

For most DOTs, knowledge of vertical clearances between the paved roadway surface and vertical structures is an important piece of information that supports the routing of oversized permit vehicles. In addition, horizontal clearances under overhead structures between fixed objects such as bridge columns, railings and median barriers are also important to ensure oversized objects do not impact the structure. Most DOTs use this information for posting clearance signs identifying the vertical clearance of structures and utilize the horizontal clearance information to route oversized vehicles. There are also Federal reporting requirements as part of the National Bridge Inventory (NBI) program that is administrated by FHWA. Many DOTs measure vertical clearances as a single, minimum value under each bridge or overhead structure. This is typically measured by field personnel who are exposed to moving traffic, lane closures and traffic delays, which create safety issues along the road. This manual measurement methodology can also be inaccurate because of the “human factor” involved in making these measurements. The position of the minimum value gets applied to the entire structure, even though it may be in a position that can easily be avoided with proper planning. This methodology can also create situations where a manual measurement methodology may not identify the true minimum clearance because it was missed because of the measurement technology limitations. There are a handful of DOTs in the industry who are using mobile LiDAR technology to inventory their overhead obstructions using mobile LiDAR and right-of-way imagery. This blend of technology is a cost-effective way to precisely measure these clearances while effectively increasing safety for workers and the traveling public. The information gathered here can be used to:

Update the NBI database,

Routing of oversize permit vehicles

Bridge Vertical Clearance Signage

Maintain an Inventory of Overhead Sign and Bridge inventory.

The next set of graphics illustrates the typical technology solution utilized for these projects. It is composed of the Riegl VMX-450 LiDAR unit. This system can collect at rates up to 1.1 KHz (1,100,000 pts/sec) at a precision of 5mm. It collects points in a circular (360-degree) pattern along the right-of-way from 2 scanner heads facing forward and to the rear of the vehicle in a crossing pattern. The laser captures 3D points at a point density along the ground of approximately 0.001 feet at speeds up to 70mph. This scanner can be adjusted to scan at a rate that is applicable for the project specifications to limit the amount of data collected and to ensure that the resulting point cloud data is manageable. Right-of-Way imagery is also co-collected along with this LiDAR point cloud data. These images are used to identify appropriate attribution for each feature type being collected. They are also used to identify the real-world features that are measured and the exact location of the minimum vertical clearance for the Bridge or Overhead structure. The following graphic illustrates these concepts.

Dual Head Scanners – One-Pass Technology

Dense Point Clouds for Precise Measurements

High-Resolution Right-of-Way Imagery is Used to Identify Clearance Structure for Measurement

LiDAR Point Cloud of Same Overhead Structure with Clearance Measurements

Right-of-Way Imagery Fused with LiDAR Point Cloud for Photo-Realistic View

Bridge Clearance Measurements (LiDAR Intensity View)

Bridge Clearance Measurements (LiDAR Fusion View)

Local Orthophotography and LiDAR data co-registered to support the data extraction process

Google Earth is used as a reference during compilation to verify bridge location and layout.

Once the data has been captured in the field, it is post-processed back in the office using a semi-automated approach. The Overhead Structure or bridge is classified in the point cloud using a manual process. The overhead points are classified into an “Overhead/Bridge” class. Then, the software automates the analysis of finding the lowest clearance point for a column of the data set.

Bridge Structures Illustrated in Point Clouds

For example, the user can set a preference to search a radius of 1-foot and then the software will automatically find the closest ground point corresponding to the column of data. The minimum clearance value will be identified and recorded in the software for that column of data. This process is automatically repeated for the remainder of the structure until the minimum clearance point has been identified and located in the point cloud. The user can specify the output of the data as either a single minimum clearance of that structure, or can identify the lowest point vertically along a horizontal distribution of measurements. An example of this would be to return the lowest point per lane across a roadway for a particular structure. In conclusion, mobile LiDAR and Right-of-Way imagery are a safe and accurate way to measure the horizontal and vertical clearance of overhead and bridge structures. This methodology promotes a safe working environment for both the DOT worked and the traveling public. It is also a cost-effective way to collect large amounts of 3D point cloud data and process it efficiently as it is applied on a per-structure basis.

Many utilities collect their infrastructure inspection data using a variety of techniques, sources and systems of record. Having many different repositories of digital information makes it difficult to make informed decisions about where to spend operations and maintenance (O & M) and capital project dollars. Having a “crystal ball” that aggregates all of this data into one single user interface could help these utilities make more informed decisions for their infrastructure as a whole, instead of using one inspection type to make these decisions.

For example, utilities typically collect information related to their structures and spans using one or a combination of these inspection techniques:

Patrols

Corona

Infrared Inspections

Climbing Inspections

Walking Inspections

Vegetation Points-of-Interest (LiDAR and Visual) Inspections

NERC encroachments (LiDAR) Inspections

Comprehensive Visual Inspection (CVI)

All of these inspections generate a large amount of data independent of one another and can be very useful if combined based on a unique structure or span number. Once combined, this information can then be used to determine the best way to bundle work activities to achieve the greatest return-on-investment (ROI).

Work bundling is a concept that has been well understood in the utility industry but not commonly practiced due to the disparate ways in which inspection data is collected and accessed from within a single agency. Many work management systems only focus on the recording of work order information related to the labor, equipment and materials used to perform a project, but do not contain strategic planning tools. These tools allow an agency to conduct “what-if” scenarios by applying different budget amounts against a planned work matrix.

Once the optimal work matrix is determined, a workplan for that utility can then be planned and programmed, executed and tracked as a project or a series of projects for that planning horizon. All costs related to that work matrix can be applied to each asset and tracked against an overall workplan budget. These actual costs are then compared to the estimated costs to refine the planning matrix unit costs that are feeding the budget forecasting model.

As an agency completes the work for that particular period, it can then record the work activities against a particular asset which determines its next activity that is due in its life-cycle. As this feedback loop is established, more cyclical work can be planned and programmed for future fiscal years and budget plans.

This concept has been applied at many utilities through the US using an asset management software called VUEWorks. This software is GIS-centric at its core and allows users to connect their GIS data to their asset management system through the use of Esri GIS software. The utility creates a map service which is consumed by VUEWorks and provides a mapping framework from which users can view inspection data from various sources.

For example, a helicopter inspection company collects CVI data by flying next to the transmission structures and collects high-resolution imagery of any defects located on that structure or its associated span. Another vendor collects walking inspection information which includes subterranean excavations around a structure and its supports. These inspections yield different defects which may require different types of activities to correct them. This is where the concept of work bundling can be used.

Since each inspection yielded different defects, the structure or span will need to be worked on at some point. It is important that all departments responsible for line maintenance understand all of the defects present on a particular structure or span so that they can conduct all work activities at the same time. In essence, VUEWorks provides this exact information, all in one place. The utility has the ability to link all of this data together based on a structure or span ID and can then view all inspection data from one single user interface.

This concept is important because if a utility needs to de-energize a line for maintenance or capital improvements, it will want to ensure that all issues are resolved during one outage. Multiple outages cost money and this concept of work bundling is helping utilities achieve high ROIs for these projects by combining projects into one single project, instead of multiple projects.

In conclusion, the concept of work bundling saves utilities time and money through the aggregation of data into a single user repository. This information can easily and effectively be used to make informed decisions and avoid multiple outage situations. By combining multiple inspection data sets together, utilities can more proactively manage their assets cost-effectively while extending the useful life of their infrastructure investment.

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Throughout the years, I have seen many projects advertised, awarded, executed and then delivered to the client. The client receives the data, copies it locally and then final payment is made to the vendor and life goes on as usual. Then, someone actually checks the data and notices that there are many discrepancies associated with the scope of work and what was actually delivered. How does this happen and how can it be avoided?

Step 1 – Start with a Clear Scope of Work

The scope should define exactly what is going to be collected, how it will be collected and how it will be verified and checked after delivery. For example, a simple LiDAR scope must define the target point densities (LiDAR), hydro-flattening parameters, and accuracies (absolute and relative) for the project. The scope should also define how the client will be checking the data for final acceptance of the deliverables.

Step 2 – Process a Pilot Area

The pilot area should be representative of the overall project and should be processed and delivered as if it was its own project. This allows for the team to identify any processing issues or special techniques up-front so that the rest of the project can move forward in a linear fashion, thus limiting the re-visiting of the data to fix problems at a later date. Once the pilot area is delivered, it should be checked against the scope of work to ensure that all deliverables are being met in accordance with the client’s expectations.

Step 3 – Process the Entire Project

Final processing can occur once the pilot area is collected and accepted. This is a critical-path item that is the bulk of the project’s budget. Many projects will either be successful or a turn into a disaster during this phase. The risk is easily mitigated, though, as long as the first two steps of this process are in place and properly executed by the team. This is very reliant on communication between the vendor and the client and if these channels are in place, the project will most likely run smoothly since everyone is on the same page.

Step 4 – Data Validation and QA/QC

This is where the overall success of a project is either validated or issues are identified that must be resolved before final delivery is accepted. The processes for checking these data sets are specific for different type of deliverables – we will focus on some niche market deliverables and give examples of how to check their associated data elements.

LiDAR QA/QC

First off – make sure you have some kind of software that can open this data. Seems simple, but many clients do not have the most rudimentary piece of the puzzle – LiDAR viewing software. There are many commercial-off-the-shelf (COTS) products that can be used and each one has its strengths and weaknesses. The goal is to be able to load the entire project in one place and then use the tools within the software to verify the deliverables. The most important items to check include:

· Average Point Density across the project

· Relative (flight line to flight line) accuracies – this should be half of the stated RMSE for the project (e.g. 5cm for a 9.25cm RMSEz spec or 7.5cm for a 15cm RMSEz spec.)

· Absolute (overall project) accuracies against ground control. Ground control should be on a hard surface and un-obscured and is typically tested to a 95% absolute accuracy specification). A minimum of 20 points is required, since one point out of 20 will get you to the 95% specification. Larger areas can require significantly more control.

· Metadata for all project deliverables (this can be automated with a metadata parser).

In conclusion, it is important to check your data immediately upon receipt, so that all quality control and quality assurance activities can be performed and verified while the data is still relevant. Good luck!

Now that the NERC alert bubble has burst, the transmission and distribution sectors of the power industry has a wealth of information that can be leveraged to enhance their business operations. Most power companies are using LiDAR, Imagery and GPS data to collect detailed information about their infrastructure and this information can be leveraged to develop a GIS-centric Asset Management database. So, what can an agency do to leverage this information, especially when it comes from multiple vendors, sensors and vintages?

First, it is important to find the common denominator between all of the data the agency is working with. Utility data typically uses a Structure ID or Span ID that can be used to tie all of this information together from a database perspective. The location of the Structure or Span can also be used to tie information together geographically from a mapping perspective as well as temporally for those agencies collecting information annually or as part of a particular inspection time series.

Next, the agency can visualize all of this information spatially utilizing a GIS so that spatial patterns can be observed. Typical spreadsheet-based deliverables are missing the spatial relationships that can be used to develop better maintenance and operation plans by observing how assets interact with one another. This spatial perspective adds another valuable dimension to help agencies prioritize where to spend their limited resources.

Finally, a Risk-Based prioritization model can then be developed to help the agency decide where to spend their limited funding resources. The assets that pose the highest risk score based on the Probabilities of Failure and the Consequences of those failures can be prioritized, thus limiting the risk to the agency based on these types of failures.

LiDAR data can be captured from fixed-wing aircraft or helicopter platforms, depending on the required resolution of the data. Most agencies are interested in capturing information about features that are located within the right-of-way of a powerline or its associated structures. These features are classified in the point cloud and then modeled using encroachment measurement criteria to identify potential hazards to the powerline infrastructure.

The LiDAR point cloud can be used to model the existing as-built structures, tops of towers, conductors, as well as the bare-earth ground model of the area. This information is then loaded into PLS-CADD software and modeled at a maximum load (sag) and maximum blowout conditions. Any LiDAR features that intersect with these “safe zone envelopes” are flagged as encroachments and will be highlighted in the PLS-CADD reports. These reports are exhaustive in terms of the amount of good information contained within them, but can be overwhelming to an agency when trying to figure out “where” to start focusing their time and resources on corrective actions.

Once all of this analysis has been performed, these encroachment features can be geospatially located and mapped for further analysis. For example, vegetation encroachments can be identified as either “grow-in” or “fall-in” potentials and these points are classified as such.

Vegetation Encroachment Management

GIS mapping provides the user the spatial context necessary to make informed Operations and Maintenance decisions. As an example, the location of vegetation encroachments is known and with a little manipulation, the volume and area of the vegetation can be determined very easily. This gives an agency the ability to control the costs associated with their vegetation management program. Since the agency knows so much about their encroachments, they can very accurately determine the volume of vegetation that needs to be removed.

The agency also knows other geospatial characteristics of the vegetation units and can then apply specific cost factors to the removal process. In addition, GIS also provides a great way to provide contractors with maps and exhibits that will help them generate more accurate bids based on relevant information. A typical vegetation removal contract is assigned to a forestry company who heads to the field and clears vegetation based on their perception of what needs to be removed. Now, agencies can tell the forestry companies exactly how much (estimated) vegetation needs to be removed and WHERE it is.

Risk-Based Asset Prioritization of Work Activities

Once your agency has identified where the encroachment issues are, how do you design a plan of action that gives your agency the biggest bang for your buck? In other words, there may be a section of powerline that contains many different encroachment types – Vegetation, Building, Ground Clearance, etc. Another section of line may only have Vegetation encroachments. The agency is most likely handling the corrective actions for these issues out of multiple departments and for good reason. Each type of encroachment brings its own set of design standards or engineering challenges to the table and all of these needs to be considered when designing a corrective action program for the facility.

One criterion that can be applied to this information is the concept of Risk. Risk takes into consideration the consequences of failure of a particular asset and then provides a Criticality Index for specific Asset Classes and Asset Types. The more critical the Asset – the higher the priority it gets when determining an agency’s primary work focus. In other words, this concept helps to identify the most critical components of your infrastructure and helps you to prioritize its maintenance over less critical assets. By prioritizing using Risk, an agency can take measures to minimize the Risk that exists in its Asset portfolio by fixing these pieces and parts first.

Risk models can be very complicated or very simple. It is dictated based on the information you wish to maintain moving forward and can use multiple automated inputs to help ease the data management strain moving forward. For example, an agency is using their LiDAR information to calculate the risk to a facility based on the number of LiDAR points that have been identified as encroachments as well as their height above ground; the higher the point, to more risky it is to the facility. In other words, the higher the vegetation feature, the more risk it poses to the facility. Since LiDAR data is composed of 3D points, the densities of these points can be applied to the facility’s risk score and then used to help prioritize the facilities that need the most work immediately.

Developing a Project Matrix and Estimating Costs Using Budget Forecasting

Once the facilities have been prioritized using the Risk concepts described above, the agency can then start planning for the actual work activities that will need to happen as part of their annual capital improvement planning activities. This can be achieved by using the Risk scores to determine which facility needs to be worked on and how much it will cost to improve that facility.

First, the facility components can be modeled from the LiDAR point cloud. As a simple example, we can imagine a distribution facility composed of a wood pole, conductors, cross-arm, guy wires and associated hardware. Each one of these facility components has a cost component associated with it based on the materials used and the characteristics of how it was constructed. The cost of materials can then be applied to each component and an overall facility cost can then be determined for the asset.

Once the facility templates are constructed, the agency can then start developing projects to improve or replace these facilities based on the results of the inspection information. This activity will allow the agency to determine the cost of a project in relation to their annual maintenance and operations budgets and then determine what they can improve for that fiscal years’ time frame.

All of this information can then be used to determine future years’ capital improvement plans based on funding availability and projected costs over time. This helps the agency to plan for future fiscal expenditures using a repeatable and defensible model that can be applied to different Asset Classes and Asset Types. In other words, multiple, disparate data sources can be fused to support the risk-based prioritization of work activities.